Project summary Quantitative PET has become increasingly important in clinical management and research, in particular for predicting and assessing response to therapy for cancer patients. Current PET protocols involve injection of PET tracers that typically result in ~6-7 mSv radiation dose to patients. For patients who require multiple repeated PET scans to monitor the response to therapy, and for patients who need PET scans with two or more tracers (e.g., FDG + FLT) to optimally predict response to therapy, it is critical to reduce the radiation dose from the PET tracer injection, while still maintaining the quantitative accuracy and image quality for cancer management. When reducing injection dose, the PET images will have higher noise due to fewer detected counts, which will subsequently introduce errors in quantitative measurements. For moving organs and tumors such as those in the lung and abdomen, respiratory motion can substantially degrade quantitative accuracy, so motion correction is required. Conventional motion correction uses a gating strategy that rebins the PET data, resulting in substantially higher noise in each gate. More advanced methods incorporate motion vector estimation in the image domain for post-registration or motion compensated image reconstruction using all detected events without increasing noise. The motion vectors need to be derived from gated PET, which are even noisier when using a reduced tracer injection in low-dose studies, imposing substantial challenges for accurate and reliable voxel-by-voxel motion vector estimation. In dynamic PET studies with clinical cardiac tracers and other novel oncology and neurology tracers, quantification is even more challenging for low-dose PET as each dynamic frame only contains a small fraction of detected events so the high image noise will affect the determination of image-derived input functions and can lead to bias and high noise in parametric images. In this project, to reduce image noise and maintain quantitative accuracy in PET, we propose to develop, optimize, and evaluate multiple innovative imaging methods for low-dose PET data to achieve comparable quantitative accuracy as full-dose PET. While the imaging developments are generally applicable to all PET tracers in oncology, neurology, and cardiology, since cancer is the primary clinical application of PET, we will focus our investigation and optimization in this project on three lung cancer imaging tracers at different clinical adoption stages as examples: 1) 18F-FDG as a routine clinical tracer, 2) 18F-FMISO for hypoxia studies as a tracer for human research, and 3) 18F-PD-L1 that specifically binds to human PD-L1 in tumors and other organs as a recent first-in-human tracer. For each tracer, we will investigate 1) static PET, 2) gated and respiratory motion corrected PET, and 3) dynamic PET.